Instructions to use hkchavan/gpt-news-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hkchavan/gpt-news-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hkchavan/gpt-news-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hkchavan/gpt-news-model") model = AutoModelForSequenceClassification.from_pretrained("hkchavan/gpt-news-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
gpt-news-model
This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2710
- Accuracy: 0.898
- F1 Weighted: 0.8982
- F1 Macro: 0.8984
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Weighted | F1 Macro |
|---|---|---|---|---|---|---|
| 0.6035 | 1.0 | 150 | 0.4915 | 0.83 | 0.8288 | 0.8282 |
| 0.3753 | 2.0 | 300 | 0.4087 | 0.865 | 0.8642 | 0.8636 |
| 0.3724 | 3.0 | 450 | 0.3754 | 0.8775 | 0.8769 | 0.8763 |
Framework versions
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.23.1
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Model tree for hkchavan/gpt-news-model
Base model
distilbert/distilgpt2